Neural network-based face detection with partial face pattern
In this paper, we present a neural networkbased method to detect frontal faces in grayscale images under unconstrained scene conditions such as the presence of complex background and uncontrolled illumination. The system is composed of two stages: threshold-based segmentation and neural network-base...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/12911/1/f3046.pdf http://eprints.um.edu.my/12911/ |
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| Summary: | In this paper, we present a neural networkbased method to detect frontal faces in grayscale images under unconstrained scene conditions such as the presence of complex background and uncontrolled illumination. The system is composed of two stages: threshold-based segmentation and neural network-based classifier. Image segmentation using thresholding is used to reduce the search space. Artificial neural network classifier would then be applied only to regions of the image which are marked as candidate face regions. The ANN classification phase crops small windows of an image, and decides whether each window contains a face. Partial face
template is used instead of the whole face to make training
process easier. To minimize the probability of misrecognition, texture descriptors such as mean, standard
deviation, smoothness and X-Y-Relieves are measured and
entered besides the image as input data to form solid
feature vector. The ANN training phase is designed to be
general with minimum customization and to output the
presence or absence of a face (i.e. face or non-face). In
this work, partial face template is used instead of the
whole face. Aligning faces is done using only one point
that is “face center”. |
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